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December 06, 2023Research on natural-language understanding seeks to harness the power of large language models, while query reformulation and text summarization emerge as topics of particular interest.
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Source: New York TimesNovember 16, 2023Real-world deployment requires notions of fairness that are task relevant and responsive to the available data, recognition of unforeseen variation in the “last mile” of AI delivery, and collaboration with AI activists.
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November 10, 2023Curating the neural-architecture search space and taking advantage of human intuition reduces latency on real-world applications by up to 55%.
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December 6 - 10, 2023
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December 10 - 16, 2023
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January 4 - 8, 2024
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November 30, 2023Registration for the online courses is open now and closes on Jan. 5, 2024.
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November 16, 2023Outlive: The Science and Art of Longevity by Peter Attia named as the best science book of 2023.
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November 09, 2023Amazon Scholar received Committee of Presidents of Statistical Societies Presidents’ Award for his achievements in statistics.
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October 10, 2023The system has expanded from generating peak computation-load forecasts one year in advance to a series of forecasts that include per-minute forecasts several months into the future.
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This paper proposes a framework leveraging small samples from different Automatic Speech Recognition (ASR) data sources to predict model performance and facilitate ASR data selection decisions. By utilizing data distribution distance and a mapping technique inspired by neural scaling laws, our framework estimates the model performance for various data mixtures within the disclosed range and extrapolates
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Writing radiology reports from medical images requires a high level of domain expertise. It is time-consuming even for trained radiologists and can be error-prone for inexperienced radiologists. It would be appealing to automate this task by leveraging generative AI, which has shown drastic progress in vision and language understanding. In particular, Large Language Models (LLM) have demonstrated impressive
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Transfer learning, reinforcement learning for adaptive control optimization under distribution shift2023Many control systems rely on a pipeline of machine learning models and handcoded rules to make decisions. However, due to changes in the operating environment, these rules require constant tuning to maintain optimal system performance. Reinforcement learning (RL) can automate the online optimization of rules based on incoming data. However, RL requires extensive training data and exploration, which limits
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2023We study the convergence behavior of the celebrated temporal-difference (TD) learning algorithm. By looking at the algorithm through the lens of optimization, we first argue that TD can be viewed as an iterative optimization algorithm where the function to be minimized changes per iteration. By carefully investigating the divergence displayed by TD on a classical counter example, we identify two forces
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EuroSys 20242023Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point cloud by only allowing outputs to specific locations. To efficiently compute SC, prior SC engines first use hash tables to build a kernel map that stores the necessary General Matrix Multiplication (GEMM) operations to be executed
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December 04, 2023UT Austin-Amazon Science Hub seeks to advance research in artificial intelligence, machine learning, and large language models.
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November 30, 2023The awards support four research projects exploring the intersection of AI and health care.
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November 21, 2023Through the UW + Amazon Science Hub, the UW associate professor and Science Hub advisory board member is helping to realize a future where robots and people collaborate on tasks.
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October 24, 2023Jetter says her goals include lowering barriers to understanding technology and cultivating a more diverse workforce.
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October 16, 2023Former Amazon applied science intern Margarida Ferreira conducts research to make complex cloud resources easier to manage.
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October 05, 2023Wharton professor Jessie Handbury lends her expertise to Amazon’s PXTCS Team as an Amazon Visiting Academic.